Neighborhood Gaussian structure and video feature fusion motion identification method

Abstract

The present invention discloses a neighborhood Gaussian structure and video feature fusion motion identification method. The method comprises: a 3D LARK operator is employed to extract the local structure feature of the video, and a neighborhood structure assessment algorithm based on the multivariate Gaussian is provided for expressing the integrated structure; the neighborhood structure assessment algorithm and the 3D LARK features are subjected to the local matching and statistical process of the multi-scale template and the video to be detected to obtain the statistic probability matrixes of two object motions; and the two statistic probability matrixes are fused to extract the object, and the double restraints improve the accuracy of the object motion. The neighborhood relation restraint integration idea is provided on the traditional LARK operator, and a new motion identification model is provided. Compared to the current method, the neighborhood Gaussian structure and video feature fusion motion identification method is more accurate in extraction of the object motion, high in identification accuracy and is applied to the visible light and the infrared videos at complex scenes.

Description

technical field [0001] The invention belongs to the moving target recognition technology in the field of computer vision, in particular to an action recognition method based on fusion of neighborhood Gaussian structure and video features. Background technique [0002] Improving the accuracy of target recognition in video is the relentless pursuit of image science research. Efficient computer automatic target recognition technology is of great significance to public security and other fields. The process of target recognition is mainly divided into two methods: training and non-training. The recognition of traditional training methods is heavily dependent on the number of samples, and the classification process is prone to over-fitting problems. At this stage, target recognition technology mainly adopts new non-training methods. [0003] The LARK feature was proposed by Seo et al. in 2010. Compared with HOG features, LBP features, Haar features, SIFT features, etc., it has r...

AI Technical Summary

Problems solved by technology

However, Seo's method uses a single-scale template and cannot identify multi-scale targets

The template contains the background, and the overall matching of the target and the template is used, which results in limited applicable scenarios of the video to be tested, and the detection effect is not good for videos that are not similar to the background of the template.

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